python中实现ROC curve

生信编程日常

发布于 2020-09-03 15:24:18

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发布于 2020-09-03 15:24:18

文章被收录于专栏：生物信息学、python、R、linux生物信息学、python、R、linux

以下是使用scikit learn预测、做出决策边界并画出ROC曲线的一个示例，以鸢尾花数据集为例。

1. 导入鸢尾花的数据

import numpy as np
import matplotlib.pyplot as plt
import warnings
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics 
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
warnings.filterwarnings('ignore')

iris = datasets.load_iris()

X = iris.data
y = iris.target

X = X[y<2,:2] 
y = y[y<2] # 方便可视化

2. 标准化数据并使用SVM预测

standardScaler = StandardScaler()
standardScaler.fit(X)
X_standard = standardScaler.transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_standard, y, test_size=0.75, random_state=1)
svc2 = LinearSVC(C=0.001)
svc2.fit(X_train, y_train)

3. 做出决策边界

# 决策边界函数
def plot_boundary(model, X, y):
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    
    h = .02  # step size in the mesh
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure(1, figsize=(4, 3))
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Set3)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Greens)
    plt.show()

plot_boundary(svc2, X_train, y_train)

4. ROC曲线

y_pred_proba = poly_kernel_svc.predict_proba(X_test)[::,1]
fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)
auc = metrics.roc_auc_score(y_test, y_pred_proba)

plt.plot(fpr,tpr,label='SVM model AUC %0.2f' % auc, color='blue', lw = 2)
plt.plot([0, 1], [0, 1], color='black', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating Curve')
plt.legend(loc="lower right")
plt.show()